PROJECT SUMMARY: Cardiovascular disease (CVD) refers to a range of complex disorders that together represent the leading cause of death worldwide. Genetic loci contributing to disease risk have been identified in genome wide association studies. Many of these loci lie in noncoding regions of the genome and have unknown function, but are thought to influence disease risk through regulatory activity. Such regulatory variants may only act in a particular cell type or at a particular time. There is strong evidence that regulatory variants acting during development can impact heart health both in the short term and in the long term, contributing to both congenital heart diseases and later-onset CVDs. However, developmental processes in human cells have been historically understudied due to ethical limitations regarding the use of human fetal tissue. Today, these ethical limitations can be overcome using human induced pluripotent stem cells (iPSCs) as a model. iPSCs can be differentiated into a variety of cell types including cardiomyocytes, the muscle cells that make up most of the heart by mass. Protocols for differentiation of iPSC derived cardiomyocytes are well established, highly efficient, and the resulting cells are a faithful model of primary human heart tissue. I propose to study gene regulation in a timecourse of cardiomyocyte differentiation using human iPSCs as a model. In my Aim 1, I will apply genome-wide techniques to assay two regulatory phenotypes - gene expression and chromatin accessibility - every 24 hours during the 16 day differentiation in a sample of 70 individuals; this will provide a high-resolution profile of inter-individual variation in temporal dynamics of gene regulation in differentiating cardiomyocytes. Then, in Aim 2, I will identify genetic variants that are associated with the developmental dynamics of gene regulation using a quantitative trait loci (QTL) analysis framework. I will first identify standard, static QTLs within each time point of the differentiation. Next, I will analyze data from all time points together to identify dynamic QTLs, QTLs that are associated with differences in gene regulation at some but not all time points. Finally, in Aim 3, I will combine the QTLs I discovered in my data with published GWAS for CVD risk to understand the ways in which regulatory variants controlling developmental dynamics contribute to cardiovascular diseases. Overall, this study leverages powerful genomic technologies and recently developed iPSC techniques to uncover the mechanisms through which regulatory variants acting during development impact disease risk, ultimately deepening our understanding of the molecular underpinnings of CVD.